Digital or film-based mammography is widely employed for breast screening to reduce the cancer mortality among women. A sizable percentage of abnormality in mammograms are comprised of microcalcifications (mcc) that are deposits of calcium in breast carcinoma. A given cluster of microcalcifications might be associated with a malignant or benign case. Distinguishing between malignant and benign clusters is difficult and time-consuming for radiologists, which may lead to a high rate of unnecessary biopsies that may be avoided or at least minimized if a computer aided detection (CAD) mammography system is employed in diagnosis. Thus, it is beneficial to design the CAD algorithm in such a way that a high true-positive (TP) rate can be achieved while the number of false positives (FP) is kept to a minimum.
It is noted that some false-positive microcalcification (mcc) candidates as selected by some mammography CAD systems were found to fall on the curvilinear normal structures in digital or film-based mammograms. Researchers in the medical imaging community have been investigating methods of identifying curvilinear structures in mammograms in order to remove microcalcification candidates that are fall on the curvilinear structures so that false positives can be reduced.
Various methods for extracting curvilinear structures have been proposed in the past. Zwiggelaar, Parr, and Taylor (R. Zwiggelaar, T. C. Parr, and C. J. Taylor, “Finding orientated line patterns in digital mammographic images,” Proc. 7th Br. Machine Vision Conf., 1996, pp. 715-724) have compared the performance of several different approaches to the detection of linear structures in mammographic images. Results obtained using synthetic images suggest significant differences between the different approaches. Approaches based on the Orientated Bins method that produces the best line orientation results and the Line Operator method that produces the best line strength results have been evaluated by Zwiggelaar et al. One approach has been implemented as a multi-scale operator and gives intuitively convincing results. The output could be used directly in existing algorithms for classifying linear structures and their spatial patterns.
U.S. Patent Application Publication No. U.S. 2002/0159622 (Alexander Schneider et al.) is directed to a system and method for detecting lines in medical images. A direction image array and a line image array are formed by filtering a digital image with a single-peaked filter, convolving the resultant array with second-order difference operators oriented along the horizontal, vertical, and diagonal axes, and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations.
The aforementioned methods demonstrate limited success with regard to efficacy and efficiency because of the enormous variations of curvilinear structures to be detected in terms of shape geometric, topological properties and pixel luminance properties.
Therefore, an improved general approach of image linear structure detection in mammography is needed. The present invention is designed to overcome the problems set forth above.